I. Field of the Invention
This invention is directed to systems and methods for routing incoming calls, such as at a customer care center. More particularly, this invention is directed to systems and methods for routing calls based on weights assigned to events that occur during such calls.
II. Description of Related Art
Customer care centers that receive incoming calls from customers are a common operation in many businesses. For small to medium size businesses, customer care centers may be staffed by a relatively small number of people with little capital equipment being used. However, for some businesses, such as large corporations selling consumer products and/or services (e.g., telecommunications services), customer care centers may have numerous customer service representatives (CSRs) in different business areas, which may even be geographically diverse. Such larger customer care centers often utilize automated call handling equipment for receiving and routing incoming calls.
Such automated call routing equipment often takes the form of one or more Integrated Voice Response (IVR) systems. Such IVR systems are known and provide for accepting an incoming call (e.g. from a customer) and obtaining information from the caller to be used in routing the call. To obtain this information, the IVR system uses one or more prompt/response sequences. A prompt/response sequence consists of the IVR providing a prompt (such as an audio prompt) and receiving a response from the caller (customer or potential customer) to the prompt. For example, the prompt may ask the user for an account number. In response, the customer may either state their account number or may enter it using dual-tone multi frequency (DTMF) signals by depressing telephone keys. Once the response is received, a predetermined (e.g., fixed) hierarchy is traversed and the customer is routed to a next prompt/response sequence. Based on the prompt/response hierarchy of the IVR, the caller is, at different points in the hierarchy, routed to a CSR or a call queue for a group of CSRs.
In this regard, for relatively large customer care centers, different types of CSRs may exist. For example, there may be general CSRs that handle routine matters. Also, there may be specialist CSRs that are responsible for addressing customer concerns in specific areas of the business, such as billing CSRs or new product specialist CSRs.
One drawback of a fixed prompt/response hierarchy is that the potential exists to route a customer call to an inappropriate CSR (or a less appropriate CSR to handle a specific issue). By way of example, a situation where a customer calls with a question regarding his/her invoice for a new product purchase is considered. It is assumed this customer traverses an IVR prompt/response hierarchy and indicates that they are calling about a billing issue. In such a fixed hierarchy system, the customer would be routed to a billing CSR when it may be more appropriate for them to speak with a new product specialist CSR. Of course, numerous other examples of such potential inappropriate routing exist.
Another drawback of fixed hierarchy systems is that they do not provide for any prioritization or classification of customer calls. For example, if a company offers premium services that customers pay for, or are granted based on, e.g., the volume of business the customers do with the company, a fixed hierarchy system does not readily allow for preferential handling of calls from such customers.
One approach that has been proposed to address such shortcomings in call routing is the use of call vectoring. Call vectoring is described, for example, in Discriminative Techniques in Call Routing, Stephen Cox—April 2003 and The Use of Confidence Measures in Vector Based Call-Routing, Stephen Cox and Gavin Cawley—September 2003. Call vectoring is a natural-language interpretation based call routing technique. In this respect, a call vectoring based routing technique routes calls based on words and/or strings of words that are extracted from what is spoken by a caller. In such approaches, the caller speaks in “natural” language, as if talking to a CSR (a person), rather than to a computer based language interpreter. Based on the extracted words and/or strings of words, calls are routed over a routing matrix using predictive statistics. Because such approaches operate using natural language as input, they are highly prone to error due, in part, to the innumerable variations in possible descriptions that must be accounted for, as well as speech patterns and vocal inflections. For example, errors of over 10% were seen in a testing environment for such approaches (See Cox and Cawley, § 3.1). The error rate using such techniques in practical applications would likely be much higher given the infinite possible combinations of descriptions, as well as the varied speech and language patterns of a diverse customer base. Based on the foregoing, alternative approaches for implementing a customer care call center are desirable.